{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "The Benjamini Yakutieli (BY) procedure is a multiple testing procedure that can be used to control the accumulation in type 1 errors when comparing multiple hypothesis at the same time.\n",
    "\n",
    "In the tsfresh filtering the BY procedure is used to decide which features to use and which to keep. \n",
    "\n",
    "The method is based on a line, the so called rejection line, that is compared to the sequence of ordered p-values. In this notebook, we will visualize that rejection line."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/mchrist/Documents/Research/tsfresh/venv/lib/python2.7/site-packages/statsmodels/compat/pandas.py:56: FutureWarning: The pandas.core.datetools module is deprecated and will be removed in a future version. Please use the pandas.tseries module instead.\n",
      "  from pandas.core import datetools\n"
     ]
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from tsfresh.examples.robot_execution_failures import download_robot_execution_failures, load_robot_execution_failures\n",
    "from tsfresh import defaults, extract_features\n",
    "from tsfresh.feature_selection.relevance import calculate_relevance_table\n",
    "from tsfresh.utilities.dataframe_functions import impute\n",
    "from tsfresh.feature_extraction import ComprehensiveFCParameters\n",
    "\n",
    "matplotlib.rcParams[\"figure.figsize\"] = [16, 6]\n",
    "matplotlib.rcParams[\"font.size\"] = 14\n",
    "matplotlib.style.use('seaborn-darkgrid')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Parameter setting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "FDR_LEVEL = defaults.FDR_LEVEL\n",
    "HYPOTHESES_INDEPENDENT = defaults.HYPOTHESES_INDEPENDENT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Load robot data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>time</th>\n",
       "      <th>F_x</th>\n",
       "      <th>F_y</th>\n",
       "      <th>F_z</th>\n",
       "      <th>T_x</th>\n",
       "      <th>T_y</th>\n",
       "      <th>T_z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>61</td>\n",
       "      <td>-3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-2</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>-1</td>\n",
       "      <td>-1</td>\n",
       "      <td>63</td>\n",
       "      <td>-3</td>\n",
       "      <td>-1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id  time  F_x  F_y  F_z  T_x  T_y  T_z\n",
       "0   1     0   -1   -1   63   -3   -1    0\n",
       "1   1     1    0    0   62   -3   -1    0\n",
       "2   1     2   -1   -1   61   -3    0    0\n",
       "3   1     3   -1   -1   63   -2   -1    0\n",
       "4   1     4   -1   -1   63   -3   -1    0"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "download_robot_execution_failures()\n",
    "df, y = load_robot_execution_failures()\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Extract Features"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Feature Extraction: 100%|██████████| 528/528 [00:26<00:00, 20.10it/s]\n",
      "WARNING:tsfresh.utilities.dataframe_functions:The columns ['F_x__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"intercept\"'\n",
      " 'F_x__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"rvalue\"'\n",
      " 'F_x__agg_linear_trend__f_agg_\"max\"__chunk_len_50__attr_\"slope\"' ...,\n",
      " 'T_z__fft_coefficient__coeff_9__attr_\"imag\"'\n",
      " 'T_z__fft_coefficient__coeff_9__attr_\"real\"'\n",
      " 'T_z__spkt_welch_density__coeff_8'] did not have any finite values. Filling with zeros.\n"
     ]
    }
   ],
   "source": [
    "X = extract_features(df, \n",
    "                     column_id='id', column_sort='time',\n",
    "                     default_fc_parameters=ComprehensiveFCParameters(),\n",
    "                     impute_function=impute)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(88, 4728)\n",
      "(88, 1921)\n"
     ]
    }
   ],
   "source": [
    "# drop constant features\n",
    "print(X.shape)\n",
    "X = X.loc[:, X.apply(pd.Series.nunique) != 1] \n",
    "print(X.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Calculate p-values and Benjamini-Yekutieli Procedure\n",
    "\n",
    "tsfresh has implemented two different feature significance tests, the Mann-Whitney-U test and the Kolmogorov-Smirnov test. In the following, both of them are being illustrated to show a scientific report of the feature selection process and to give a comparison of the differences of both methods."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Mann-Whitney-U\n",
    "Run significance test with Mann-Whitney-U test. Returns the p-values of the features and whether they are rejected or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_y__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_y__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_x__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_z__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_z__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_z__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_x__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_y__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_z__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_y__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_x__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_x__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_z__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_z__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_y__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([1.0, 0.99999999999999989]) in feature ''F_y__autocorrelation__lag_0''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_x__fft_coefficient__coeff_0__attr_\"angle\"''.\n"
     ]
    }
   ],
   "source": [
    "df_pvalues_mann = calculate_relevance_table(X, y, fdr_level=FDR_LEVEL, test_for_binary_target_real_feature='mann')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('# total \\t', 1921)\n",
      "('# relevant \\t', 595)\n",
      "('# irrelevant \\t', 1326, '( # constant', 0, ')')\n"
     ]
    }
   ],
   "source": [
    "print(\"# total \\t\", len(df_pvalues_mann))\n",
    "print(\"# relevant \\t\", (df_pvalues_mann[\"relevant\"] == True).sum())\n",
    "print(\"# irrelevant \\t\", (df_pvalues_mann[\"relevant\"] == False).sum(),\n",
    "      \"( # constant\", (df_pvalues_mann[\"type\"] == \"const\").sum(), \")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>rejected</th>\n",
       "      <th>type</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F_x__abs_energy</th>\n",
       "      <td>F_x__abs_energy</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>5.900086e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F_x__range_count__max_1__min_-1</th>\n",
       "      <td>F_x__range_count__max_1__min_-1</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.268453e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F_y__abs_energy</th>\n",
       "      <td>F_y__abs_energy</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.327674e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_y__variance</th>\n",
       "      <td>T_y__variance</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.778390e-12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_y__standard_deviation</th>\n",
       "      <td>T_y__standard_deviation</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.778390e-12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                         Feature  rejected  \\\n",
       "Feature                                                                      \n",
       "F_x__abs_energy                                  F_x__abs_energy      True   \n",
       "F_x__range_count__max_1__min_-1  F_x__range_count__max_1__min_-1      True   \n",
       "F_y__abs_energy                                  F_y__abs_energy      True   \n",
       "T_y__variance                                      T_y__variance      True   \n",
       "T_y__standard_deviation                  T_y__standard_deviation      True   \n",
       "\n",
       "                                 type       p_value  \n",
       "Feature                                              \n",
       "F_x__abs_energy                  real  5.900086e-12  \n",
       "F_x__range_count__max_1__min_-1  real  6.268453e-12  \n",
       "F_y__abs_energy                  real  6.327674e-12  \n",
       "T_y__variance                    real  6.778390e-12  \n",
       "T_y__standard_deviation          real  6.778390e-12  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pvalues_mann.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Kolmogorov-Smirnov\n",
    "Run significance test with Kolmogorov-Smirnov test. Returns the p-values of the features and whether they are rejected or not."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_y__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_y__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_x__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_z__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_z__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_z__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_x__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_z__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_y__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_y__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_x__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_x__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''T_z__ratio_beyond_r_sigma__r_2.5''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''F_z__fft_coefficient__coeff_0__attr_\"angle\"''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 0.066666666666666666]) in feature ''F_y__ratio_beyond_r_sigma__r_3''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([1.0, 0.99999999999999989]) in feature ''F_y__autocorrelation__lag_0''.\n",
      "WARNING:tsfresh.feature_selection.significance_tests:[target_binary_feature_binary_test] A binary feature should have only values 1 and 0 (incl. True and False). Instead found set([0.0, 180.0]) in feature ''T_x__fft_coefficient__coeff_0__attr_\"angle\"''.\n"
     ]
    }
   ],
   "source": [
    "df_pvalues_smir = calculate_relevance_table(X, y, fdr_level=FDR_LEVEL, test_for_binary_target_real_feature='smir')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "('# total \\t', 1921)\n",
      "('# relevant \\t', 1058)\n",
      "('# irrelevant \\t', 863, '( # constant', 0, ')')\n"
     ]
    }
   ],
   "source": [
    "print(\"# total \\t\", len(df_pvalues_smir))\n",
    "print(\"# relevant \\t\", (df_pvalues_smir[\"relevant\"] == True).sum())\n",
    "print(\"# irrelevant \\t\", (df_pvalues_smir[\"relevant\"] == False).sum(),\n",
    "      \"( # constant\", (df_pvalues_smir[\"type\"] == \"const\").sum(), \")\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Feature</th>\n",
       "      <th>rejected</th>\n",
       "      <th>type</th>\n",
       "      <th>p_value</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Feature</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F_x__abs_energy</th>\n",
       "      <td>F_x__abs_energy</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>2.343896e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_y__variance</th>\n",
       "      <td>T_y__variance</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.491072e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F_y__abs_energy</th>\n",
       "      <td>F_y__abs_energy</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.491072e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F_x__standard_deviation</th>\n",
       "      <td>F_x__standard_deviation</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.491072e-15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>T_y__standard_deviation</th>\n",
       "      <td>T_y__standard_deviation</td>\n",
       "      <td>True</td>\n",
       "      <td>real</td>\n",
       "      <td>6.491072e-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                         Feature  rejected  type       p_value\n",
       "Feature                                                                       \n",
       "F_x__abs_energy                  F_x__abs_energy      True  real  2.343896e-15\n",
       "T_y__variance                      T_y__variance      True  real  6.491072e-15\n",
       "F_y__abs_energy                  F_y__abs_energy      True  real  6.491072e-15\n",
       "F_x__standard_deviation  F_x__standard_deviation      True  real  6.491072e-15\n",
       "T_y__standard_deviation  T_y__standard_deviation      True  real  6.491072e-15"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_pvalues_smir.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Calculate rejection line\n",
    "With the rejection line it is determined whether a feature is relevant or irrelevant."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Mann-Whitney-U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "m = len(df_pvalues_mann.loc[~(df_pvalues_mann.type == \"const\")])\n",
    "K = list(range(1, m + 1))\n",
    "\n",
    "if HYPOTHESES_INDEPENDENT:\n",
    "    C = [1] * m\n",
    "else:\n",
    "    C = [sum([1.0 / i for i in range(1, k + 1)]) for k in K]\n",
    "\n",
    "rejection_line_mann = [FDR_LEVEL * k / m * 1.0 / c for k, c in zip(K, C)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Kolmogorov-Smirnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "m = len(df_pvalues_smir.loc[~(df_pvalues_smir.type == \"const\")])\n",
    "K = list(range(1, m + 1))\n",
    "\n",
    "if HYPOTHESES_INDEPENDENT:\n",
    "    C = [1] * m\n",
    "else:\n",
    "    C = [sum([1.0 / i for i in range(1, k + 1)]) for k in K]\n",
    "\n",
    "rejection_line_smir = [FDR_LEVEL * k / m * 1.0 / c for k, c in zip(K, C)]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Plot ordered p-values and rejection line\n",
    "\n",
    "In the plot, the p-values are ordered from low to high. Constant features (green points) are always irrelevant but are not considered for calculating the rejection line (red line).\n",
    "\n",
    "For nice plotting, the p-values are divided in the three groups relevant, irrelevant and constant (which are also irrelevant)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Mann-Whitney-U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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sSuXTCfekpCTCwm4anRcREY5Go6F//y+MylPq3qBKlfepX78RBw7sw8vLm717\nd+PtXR+12hK12hJf31Zs2LCey5cvcf36NUJDL1GhQqVn4nLE1tbW8Nna2obk5OSXvt7MxJlRLC/r\n2Xt848Y14uIe0bx5Q0OZXq9Ho9Fw+3YUH33Ujr17d9GmTXMqV65KvXoN8PVt/cp9CyGEEEII8brU\nV3YATzd+0lk687Dlyjw1G5sWSWgzQVPEC1TmKe8gVpmnfM5CFhYWz31WG77XalNejDxxoj/Fi5cw\nqmdra2f0ObXuggVLjRJPeDqr2aRJMyZNGotGM4qDB/cxcuRYAKKj79C7d3dKly6Lp6cXrVu3JSjo\nMGfO/G1ow9zc3CT2V9mgKaM4MxPLsxQK08fhU/tI9ew91mq1uLm5M2PGXJPzXF0LYG5uzk8//UJw\ncBBHjx5mzZqV/PLLz6xYsTbTM8JCCCGEEEK8CWZRIVidCkB15zTwdHb2ce0ReT6ZBXmGNlOSC1Yn\n9qMNPK41PMuXG2fEzs4OJ6d83L17Fzc3d9zc3ClUqDCBgYsIDb1kVLdIETdUKhUPHsQa6jo6OrFg\nwWyioiIBqF69JgqFkg0b1qPRaKhVKyV5P3hwH9bWNsyaNZ9OnbpQpcr7RETcIq3HzNOSVlKZnozi\nzCiW5/syNzcnPv7pzskJCQncv38v3f7d3Ytx585t7O0dDP3HxMSwZMlCdDodO3du59Ch/TRo0IhR\no8axcuX6/2aJQzN9jUIIIYQQQrwus6gQHH9uj/ra76gSY585okTrXC7b4spKktBmUnLB6iRU98tR\nyWyqjz/uyvLlSzh4cD/h4WF89923nDhxjOLF3zOqZ21tQ6tWbZgzZwYhISe4ceM6U6dO4MqVUNzd\n3YGU1wY1bOjD6tUradCgEWZmKZP49vYO3L0bzYkTwdy6Fc66dd9z4MBekpI0mYrRysqKuLg4bt68\nkeEy5IzizCgWK6uU99iGhl4iPj6ecuUqcO3aFfbs2UVY2E1mzvRHqVSl27+nZ20KFSrMpEljuXz5\n0n+bU01GqVSiVqt5/DiOefO+4/jxYCIjI9ix41esrKzT3FVaCCGEEEKIN80sKgS7Hb2x/7U76JJR\ngNEX6FIem3wHyJLjPKBLl+48efKE2bOn8+jRQ8qUKcvs2QtwcclvUtfPbzCLFs1jwoTRJCYm4eFR\nmdmzFxotlW3SpBk///wTPj4fGMoaN27K6dN/MW7caADKl6/AgAFDWLp0EYmJprspP69atZoUK1ac\nnj27sHgyUMhjAAAgAElEQVTxcsqVq/DC+i+KM6NYHBwc8fVtxcSJY+nbdwAdO3ahc+dPmDnTH5VK\nSceOXfDwqJJu3yqViunT5zB37iz69v0MtVpN/fqNGDBgMADt2nUiOjoaf/+JPHz4gPfeK8mMGXOw\nt7fP8D4IIYQQQgjxOsyiQnDc0o6U5yFTmKyZVGb9Y5LZRaF/lYcccxCNRktsbHzGFUWu4OhoLeOZ\nx8iY5i0ynnmLjGfeIuOZ98iY5i1vYjzV59djc2QSSs1jnn+gTw/oLOzRFKlDQrW+OXJl6avKn98u\n3WMyQyuEEEIIIYQQOZx1kD/Wfy02KjOamVSa87DV2jyVyGaGJLRCCCGEEEIIkYOpz683JLOpM7Op\nyazWOj/JBarnuVnZzJKEVgghhBBCCCFyqGdnZlPfMZvqSZl2xDWdny1x5RSS0AohhBBCCCFEDpTW\nzCyAXqUmofLnxNcZkz2B5SCS0AohhBBCCCFEDqI+vx7rkAUo4yKA55cZK3nQZuM7ubw4LZLQCiGE\nEEIIIUQO8eLNnxQ8ajhNktlnSEIrhBBCCCGEENksdVZW9SgceG6JMaCzdOZhy5WSzD5HElohhBBC\nCCGEyEbq8+ux2z/S8Pn5zZ8AHtceIclsGiShFUIIIYQQQohsoj6/HtvDEwHjWVkAvUKFzrYQ8dUH\nkFjxk6wPLhdQZncAwlhkZATe3jUIDw97Y216e9fgxIljb6y9V6HRaNi6dXOG9Xr16klo6OUsiCht\nmY0zs06dOsnVq6EAXL16BT+/L9Hrn/9921P379+nb9/PaNy4DkuXLk63Xmbt27ebmJi7r92OEEII\nIYR48+x/+QS7/SNRJMcDKbOyqV8oVDxot4X7nwZLMvsCktDmMK6uBdi27XcKFSqc3aG8Ubt3/8Hq\n1SteWOePP3bg4uJCqVKlsygqU5mJ82UMHNiHmJgYAEqUKEmBAgXZuXN7uvV37drJrVu3WLXqBzp3\nfr1/uKKiIhk3bhQJCQmv1Y4QQgghhHjzbHcNxCLsAJAyM2vYyVilJvG95sS22yJLjDNBlhznMCqV\nCmdnl+wO44170axk6vHVq1cwZcqULIoo/TjepnbtOjF58jhatPgQheL5RSUQFxdHkSJFKFas+Gv3\n9bavRQghhBBCvBz1+fVYnV6OIj4aVWIsYPq8bFy9STIj+xIkoc1hIiMj6NixNT/++DNubu54e9eg\nR4/P2bZtMyVLlqFZsxZs3boZV1dXTpw4hp/fYD788CNWr17B1q2bSUiIp2LFygwePBx396Im7Scl\nJREQsIBdu3ai0+mpXr0mgwcPJ18+ZyZMGI1SqWLChKdJ5axZ04iNvc+UKTM4d+4MixfP599//0Gh\nUFC58vuMHj2O/Pld2bHjV379dSuenrXZvHkDGo2GFi1aMXDgEP7++xT+/inPBXh71+Cnn34xmYEO\nCTnBo0ePqFr1fR4+fAJARMQt5s6dyV9/ncLW1pa2bTvw6aefAXDnzm0WLJjDyZPHUSoV+Ph8QP/+\n/0OtVr8wFqVSye3bUcyY4c/Zs6dRqVTUq9eA//1vOBcvXjCJ08HBgfnzZ3P48EHi4h5RqFBhvvqq\nPw0b+hjqff31N/z44zrCwm5Stmw5xo6dRJEibnTo0AqAwYP706vXF3z++VdUqFCRhIR4Tpw4hqdn\nbaN7MHXqN4bZ29T+nZ1d0h0v4IVj0rFjawA6d27LmDETiIyM4OTJ4wQEPJ2B7tChFT16fE6rVm3w\n8/uSEiVKcuzYURITE/n++/8jKSmROXNmcOLEMeztHWjatDm9e/fB3Nyc5ORk5s6dyf79e0lIiMfD\nowpDhoygaNHir/CTL4QQQgiRd5lFhaD8aSx2d84alT+bzOrUTjz2GiXJ7EuSJceZdP7+WX4IXcP5\n+2czrvyGHT58gMWLlzNo0FAALlw4h5tbUZYtW03duvXYvHkDv/++g3HjJrF06Wrc3NwYNKgvT548\nMWkrMHAR586dYfr0uSxcGIher2PEiMHo9XqaNGnG0aOH0Wg0AGi1Wg4c2IePzwfExz9m+PD/UaOG\nJ2vXbmT27IVERNxi9eqVhrb/+ec8169fZfHi5QwZMpItWzZy7NhRPDyqMHDgUJydXdi27XdcXQuY\nxBUcHES1ajVQKlN+JJOSkhgyxA+VyozAwJWMGjWOH35Yw59/7kSj0TBwYF8SEuJZsCCQyZOnExwc\nxMKFczOMBWDOnBmYmalYvnwNc+ak3I81a1amGef8+bO5ceMac+YsZO3ajVStWo3p06eSlJRk6GvV\nqmUMGDCE5cvX8PDhQwIDFwGwbNkaACZN+pYuXboDoFAoqF7dk+DgIyb3YNCgYXTu3I3y5Ssa+n/R\neGU0JsuWrQZgyZJV+Pg0zdTP2o4dvzJmzDd8++13ODg4MGbMcOzs7FmxYh0TJkwhKOgQS5YsBGDz\n5g0cPx7MzJlzWb36R6ytbZg6dWKm+hFCCCGEyOvMokKw29Ebp+WVcNz8Ecr/klkFxkuMATTOFbjX\n+6wks69AZmgz4fz9sww7NhCNToO50pxZteZT0ckjy/pv3bqtYdbr4sULAHz66WdYW1sD8MMPaxk0\naBjVq9cEYPDgERw9GsT+/Xto3ryloZ0nT56wZctGAgNXUaZMOQDGjZuEr68PZ878Te3adQE4efIY\nXl7enD79F4mJidSp401cXBzdu/eiS5duKBQKChcuQsOGjTl37oyhfa1Wy/DhX2Nra0vRosXZsGE9\nFy9ewMurLra2tiiVynSXU1+8eMEQf0oMx7l7N5rly9dia2tLiRKlGDJkJJaWVhw7FkR09G2WLl2F\nvb0DAEOGjGTkyMF89VX/DGOJjIykVKlSFCpUGHNzc6ZOnYlCocDc3NwkzsqVq9KxYxdKliwFQJcu\n3fj1163cvRtN4cJFAOjUqQs1angC0KZNBzZu/D8AnJycALCzszOMFUDx4u9x9KhpQmtra4uVlRVm\nZmY4O7tkOF5ubu4vHBNHR6f//nRErbZM874/r3btOlSpUtUwBhERtwgMXIVKpaJYseIMGTKSIUP8\n6Nt3AJGRkajVagoWLIyTkxPDho0iLOzNbWYmhBBCCJFbmUWF4LilHei1hjLDM7LPV1aoeNxwWlaF\nludIQpsJp2P+QqPToEOHRqfhdMxfWZrQFixovDzXwcHBkCDFx8dz585tJk0aa5jdhJQZzrCwm0bn\nRUSEo9Fo6N//C6PylLo3qFLlferXb8SBA/vw8vJm797deHvXR622RK22xNe3FRs2rOfy5Utcv36N\n0NBLVKhQ6Zm4HLG1tTV8tra2ITk5OVPXGBt7HwcHR8Pn69evUqSIu1F7H3zQAoB1677Hzc3dkMwC\neHhURqvVEh5+M8NYunXrgb//RA4dOoinZy0aNGiMj88HacbVvHlLDh3az6+//syNG9f599+LAOh0\nOkOdIkXcDN/b2Nig1b74mu3tHbh///6LbwiZG6+MxuRlPfuzduPGNeLiHtG8eUNDmV6vR6PRcPt2\nFB991I69e3fRpk1zKleuSr16DfD1bf3KfQshhBBC5HZmUSFYnQrAPPwQ6LWmr+H570+duR16SweS\nXSqRUK2vbP70GiShzYQqzu9jrjQ3zNBWcX4/S/u3sLB47rPa8L1Wm/Jbn4kT/SlevIRRPVtbO6PP\nqXUXLFhqlOzB09m8Jk2aMWnSWDSaURw8uI+RI8cCEB19h969u1O6dFk8Pb1o3botQUGHOXPmb0Mb\n5ubmJrFndmMihUKBTvf0N1hmZqZtpUprtlGr1Rn9+aJYmjZtTo0atTh0aD/BwUFMmzaJ48eD+frr\nb0zOmTJlAmfPnqZZM1/atOmAs7MLffr0MqrzfKyZ2QBLqTTdEMr0ml48XpkZk2eltQlVah+pnv1Z\n02q1uLm5M2PG3OdPw9W1AObm5vz00y8EBwdx9Ohh1qxZyS+//MyKFWszPSMshBBCCJFXpDUrC6Yz\nsvHv9yO+zpisCyyPk2doM6Gikwezas3nszJfZvly44zY2dnh5JSPu3fv4ubmjpubO4UKFSYwcBGh\noZeM6hYp4oZKpeLBg1hDXUdHJxYsmE1UVCQA1avXRKFQsmHDejQaDbVqeQFw8OA+rK1tmDVrPp06\ndaFKlfeJiLhFGosm0pRWMvWsfPmcefDggeGzu7s7ERHhPH4cZyhbsSKQqVO/oVix4oSHh/Hw4dP6\n58+fQaVS4ebmRkaWLl1MdPRtWrdui7//TEaOHMuePbtM4nz8OI5du35nwoQp9O7dhwYNGvHoUUqf\nr7OD8IMHsYZNnV4ko/HKaEyev+fm5ubEx8cbPickJHD//r10+3d3L8adO7ext3cw9B8TE8OSJQvR\n6XTs3LmdQ4f206BBI0aNGsfKlev/myUOfbUbI4QQQgiRi1le3GSYlU39MszImlmjc/citv02SWbf\nMEloM6mikwddS32ao5LZVB9/3JXly5dw8OB+wsPD+O67bzlx4hjFi79nVM/a2oZWrdowZ84MQkJO\ncOPGdaZOncCVK6G4u7sDKa8NatjQh9WrV9KgQSPMzFIm8e3tHbh7N5oTJ4K5dSucdeu+58CBvSQl\naTIVo5WVFXFxcdy8eSPNZchlypTjypWniZCnpxeurgWYPn0q169f4+jRI/z004/Url2HGjU8cXcv\nxuTJ4wkNvcypUyeZO3cWPj4fGC1bTk/KJk8zuHTpIjdvXmf//r2ULVvOJE4LCzWWllYcOLCPyMgI\njh8PZvbsmQBoNEkv6uKZ67bm2rWrxMU9TcxDQy9TrlyFDM/NaLwyGhMrK6v/+rtEfHw85cpV4Nq1\nK+zZs4uwsJvMnOmPUqlKt39Pz9oUKlSYSZPGcvnypf82p5qMUqlErVbz+HEc8+Z9x/HjwURGRrBj\nx69YWVmnubu2EEIIIUReZxYVYvhez9NkNv79ftz76hK6T3+TpcVvgSS0eUCXLt1p06Y9s2dPp0eP\nzly7doXZsxfg4pLfpK6f32Bq1qzNhAmj6d37UxITE5k9e6HREtEmTZqRkBBv9Fxp48ZNadbMl3Hj\nRvP5590JCTnBgAFDuHnzOomJprspP69atZoUK1acnj27mMwcQ8pmROfOnTE8m6pSqZg27TsePnzA\nZ591Y9asafTq1Rsfnw9QKpVMmzYLhULBV1/1ZPz40dStW8+wPDojw4aNxsXFlUGD+vHZZ93QarVM\nmDDVJM4rVy4zfvwkDh7cxyefdGT+/O/o0aMX+fO7cunSv5nq6+OPu7JkyUJWrgwEUmZ2z549TZ06\n3pk6/0XjldGYODg44uvbiokTx7J9+1Zq1PCkc+dPmDnTnz59elG0aDE8PKqk27dKpWL69DkolSr6\n9v2MkSMHU6XK+4walXKf27XrhK9vK/z9J/LJJx04dOgAM2bMwd7ePlPXJoQQQgiRV1gH+WMWk7J5\na2oiq7Vz41HD6TIj+5Yp9K+zdjIH0Gi0xMbGZ1xR5Gg6nY6uXTvwzTcTKFcu/SQrtzt16iQzZ/qz\nfv0mo0288jJHR2v5O5qHyHjmLTKeeYuMZ94jY5rzpW4CZXF9F4r/lhvrAa1NIe73PGFUV8bz1eXP\nb5fusXfjf9Qix1MqlXTv3pONGzdkdyhv1datm/nkkx7vTDIrhBBCCJFXpW4Cpb72O4r/NoJKnSlM\nLNM2+wJ7x8guxyLH8PVtxZ9//sbly5coXbpMdofzxl29Gkp09B1atpRX2wghhBBC5EapM7Jmd8+h\niL9r8moePSlLjWWZcdbJ0mmipKQkxo0bR82aNalbty7Lli1Lt+7Jkydp164dVatW5aOPPuLw4cNZ\nGKnIDgqFgtWr1+bJZBagRIlSBASsyHDHZyGEEEIIkfOYRYXg+HN71Nd+R/UoHKX26T4yRptAVR+Q\nLfG9q7I0oZ0xYwZ///03q1atYuLEiQQEBPDbb7+Z1IuJiaFPnz40b96cX375hRYtWtC/f39u3bqV\nleEKIYQQQgghBPDfa3l0yUav5Xn21Typm0AlVvwk22J8F2VZQhsfH8/GjRsZPXo0lSpVokmTJvTu\n3Zt169aZ1D116hQAX375JUWLFqVPnz5YWlpy+vTprApXCCGEEEIIIQBQn1+P5fm1wNPZ2OdfzXP/\n02BJZrNBliW0Fy9eJCkpierVn757qXr16pw9exatVmtU19HRkUePHrFz5070ej27d+/m8ePHlC1b\nNqvCFUIIIYQQQgjMokKwOzAaeDorC6BXmpPsVFpezZPNsmxTqOjoaBwcHFCr1YYyFxcXNBoNMTEx\nuLq6Gspr1KhBt27dGDx4MEOHDkWr1TJlyhRKliyZVeEKIYQQQggh3mHq8+uxOr0c5cMw0OueJrL/\n/RlXf4rMyOYAWZbQJiQkYGFhYVSW+jkpKcmoPD4+nvDwcPr27UvTpk05cuQI/v7+lC5dmqpVqxrV\nVakUODpav93gRZZRqZQynnmMjGneIuOZt8h45i0ynnmPjGn2UWz9CtX5n0zKDc/L1h6IVd0vsHqJ\nNmU8344sS2jVarVJ4pr62crK+EdhxYoVJCUlMWjQIAAqVKhAaGgoAQEBBAYGGtXVavXyguI8RF44\nnffImOYtMp55i4xn3iLjmffImGYtwyt5wg+j0sQBmLySB3i68dNLjo2M56vLn98u3WNZ9gxtgQIF\nePjwoVFSGx0djYWFBQ4ODkZ1z549S7ly5YzKKlasSFhYWJbEmtv4+X3J0qWLX7ud+/fvsWfPn4bP\n3t41OHHi2Gu3+7wVKwLp2/dzAHbs+JW2bX3feB/PGjDgK0JDLwPQoUMrvL1rmHx1797JEM+z5Q0a\n1KJtW1/mzp1JfPxjQ5unTp00aaNhw9p06NCKFSsC04zjVV2+fImvvuqFj09dPvusG//8c/6F9Tdt\n+pG2bX1p2rQ+/v4TSUhIMBzbtet3k7hHjx4KwNWrV/Dz+xK9Xp9e00IIIYQQeZJZVAiOW9qlvJLn\nmWQ2rc2fZJlxzpJlM7Tly5fH3Nycv/76i1q1agEQEhJCxYoVMTMzDsPV1ZXQ0FCjsitXrlC0aNGs\nCjdX8fefiZmZ+Wu3ExCwgOTkZHx8PgBg27bfsbd3yOCs1+Pj0xQvL++31v4ff+wgXz5nSpUqbSjz\n8/sfTZs2N6r37M+gs7MLK1em7L6t0Wi4du0K8+Z9x9WrV5g7dzFK5dPfA/388w7D5ydPnnDo0H4W\nLZpH4cJFaNHiw9eOPyEhgWHDBuLj05TRo8ezbdsWhg//Hxs3bsXa2sak/oEDe1m2LIBx4ybh4pKf\nqVO/YeHCOQwfnrJRwbVrV6lfvxFDh440nGNhkfJce4kSJSlQoCA7d27H17fVa8cuhBBCCJFb2AT5\ng16b5oysztwOnW1BEqr0lmQ2B8qyGVorKyvatGnDxIkTOXPmDHv27GHlypV8+umnQMps7ZMnKS8n\n7tSpE0eOHGHZsmWEhYXx008/sWXLFnr06JFV4eYq9vYOWFu//nr852fmnJ1dMDd//UT5RdRqS5yc\nnN5K23q9ntWrV9CuXSejchsbW5ydXYy+HBwcDceVSqWhvGDBQnh5eTNjxhxOn/6Lgwf3GbXl5JTP\nULdIETc6d+5G9eo1OXhw/xu5hj17/sTMzAw/v8EUL/4eAwcOwdbWlj17dqVZf+PG/6N9+4/x9m5A\nuXIVGDZsDDt3bic+PmV5y/Xr1yhZspTRtdvZPV3C0a5dJ9asWSmztEIIIYR4Z1gH+WMe+XRV4rMz\nsknuDbj35T/Edt0nyWwOlWUJLcDo0aPx8PCgR48eTJgwgf79++Prm7Lc1Nvbmx07dgBQpUoVAgIC\n2LlzJ61bt2bNmjXMmjULLy+vrAw3W0RGRuDtXYPvv19O8+aN8PefCMDBg/vp1q3Tf8tOPyE4OMhw\nzvNLjrdt20LHjh/RtGk9+vb93GiJ6pMnT5g9ezofftiE5s0bMXnyeOLjH7NiRSA7d27nzz930qFD\nyuzcs0uOExMTCQhYQLt2LWnSxJsRIwYTFRVpFPP+/Xv4+OM2NG5ch2HDBhIbG5vh9T675PjUqZP4\n+DRi27YttG3rS5Mm3kycOJbExCeG+i+6D88LCTnBo0eP8PConGEcGSlatDhVq1bLVKJqYWGBSqVK\n89jUqd+kueTZ27tGmvXPnz+Hh0cVwyywQqHAw6MK586dMamr1Wr5558LVK1azVBWsWIltFotly//\nC8D161cpWrRYurFXqFCRhIT4t7LUXAghhBAipzCLCsF+S3vyBZbB+q+U/0enLjEG0FnYE/9+Px62\nXp9tMYrMybIlx5AySzt9+nSmT59ucuzff/81+tygQQMaNGiQVaHlOH//fYoVK9b+l4xcYvLk8Qwd\nOhIPjyqcOBHMmDHDCQxcSenSxu/mPXz4IMuXL2HEiDEUL16CvXt3MXBgX/7v/7bg4uLCzJlTuXjx\nH6ZOnYm1tQ3Tpk1kwYI5DBgwhBs3rqPTaRk6dLRJPLNmTePs2dOMHTsRBwdHFi+ez8iRQwxLcwHW\nrv2e8eOnADBq1BB++GEN/foNfKnrjomJYe/eXcyaNZ+7d6MZM2YYlStXpW3bDi91HwCCg4OoVq2G\n0RLh11G8+HucPXs63eNarZbDhw9y/Hgw48ZNSrPOoEHD6NPHL9N9xsTcNUlAnZzyERp6yaRuXNwj\nkpIScXFxMZSZmZlhb+/AnTu30Wg03LoVTlDQYZYvX4Jer6dRoyZ8/vlXhh3HFQoF1at7Ehx8BE/P\n2pmOUwghhBAiNzCLCsE6yB+LSONf3j+bzMa/30/eK5uLZGlCmxM8+f03nvz2a5b2admyFZbNW77U\nOR07dqFIETcAJk8eR8uWrWn+XxtFinTgwoXzbNq0gdGjxxud98MPa+jWrQf16jUEoEePzzl58jjb\nt2+lQ4fO7N79J7NmzadKlfcBGDZsNH//fQpra2vUajVardZkCfDDhw/5448dTJ8+h2rVUmYSJ0yY\nTLt2LTl27CjvvVcCgF69vqBixUoAfPBBcy5evPBS1wyQnJzMwIFDKVmyFCVLlqJWrTr888952rbt\nwI8/rs30fQC4ePEC1avXNCmfM2cG8+d/Z1S2ceM2nJzyvTA2Gxtbw9LdVC1aNDJ8n5SURIECBRkw\nYIjhOeTn2draYmtr+8J+npWY+MRk2beFhYXJjuGAYcm+ubnx67HMzc3RaDSEhd1Eq9ViaWnFlCkz\niIgIZ96874iPjzd6prZ48fc4evRIpmMUQgghhMjJDLsX3w5BFR9tKFekUTepUC1JZnOZdy6hzS0K\nFSpk+P769etcvRrKb79tM5QlJydTvnxFk/Nu3LhGYOA/LF++xFCWlJSEq6srYWE30Gq1lC1b3nCs\nQoVKVKhQ6YWxhIXdRKfTGZJVSHlut2jRYly/fs2Q0KYm4ADW1jYkJye/xBU/9Ww7NjY2aLUp7bzM\nfQCIjb1v9Gxsql69vqBRoyZGZZnZ/Co+/rHJRkwrVqxFqVRx48Z1Zs70p27d+rRv3ymdFmDmTH/+\n/HNnmsd27TpkUmZhYYFGozEqS0pKwtLSMo26KZs7aTTGya5Go0GttqREiZL89ttuwz0pXboMer2e\nb775mkGDhho2xrK3d+D+/fvpXoMQQgghRG6Runsxeq2hLK2Nn1IOqCSZzYXeuYTWsnnLl54tzQ6p\nyQmkLGXt3LkbLVu2NqqT1oZNWq0WP7//mSwXtbKy4t69e68Ui1qtTrNcq9Wh0z39x+H5eF51Y6Hn\nd71ObeZl7gOkLJ99Nr5Ujo5OuLm5v3RcoaGXKVGipFFZ4cJumJmZ4ebmjr39t/Tv/wWurgXo0qVb\nmm307t2HLl26Z7pPFxdX7t2LMSq7dy8GZ2cXk7oODg5YWKiJiYmhRIlSQErC//DhA8My5OcT/GLF\n3iM5OZnY2FhDHb1ej1KZ1u8shRBCCCFyF8uLm0x2LwbjRFandkJTuBYJ1fqSXLB6VoYn3oAs3RRK\nvJqiRYsREXELNzd3w9cff+xIc4Mid/di3Llz26jujz+u46+/QihSpAgqlcqwQRDAiRPH6Ny5HTqd\nDoUi7SSmSBE3VCoV58+fM5Q9eBBLePhNihYt/qYvN10vcx8A8uVz5sGDB2+k77Cwm5w587fJzO6z\nPDyq0LZtB5YvDyAyMiLNOk5O+Yzif/YrLRUrVuLs2TOGXw7o9XrOnj1NxYoeJnWVSiXly1fgzJm/\nDWXnz59FpVJRunRZDhzYS6tWHxjN+F6+/C+2tnY4Ozsbyh48iCVfPmeEEEIIIXIjs6gQ7Hb0xmlN\nbSz+2WAof/6dsppCtYhtv417vc/yyHe5JLO5lCS0uUCnTl3Zv38PGzasJzw8jK1bN7FmzUrc3NxM\n6nbu/AmbNv3Izp3buXUrnJUrl/Lbb79QrFhxrK1t8PVtxbx5szh37iyXLl0kIGA+NWrURKlUYmVl\nRVRUJNHRd4zaTHnlUnvmzZvFqVMnuXIllMmTx5M/vyu1amXdztMvcx8AypQpx5UroWkeexGdTkdM\nzF1iYu4SFRXFkSOHGD16KNWr16Ru3XovPLd3775YWVkzf/7sl+43LY0a+ZCQEM+cOTO4du0qCxbM\nJj4+niZNUp7RTUx8QkzMXUP9lGeN13PgwF4uXrzAd999i69va6ytralatRp6vZ4ZM6Zy8+YNgoIO\ns2jRPLp27W70y4zQ0MuUK1fhjcQvhBBCCJGVUpcYq6/9jupROEpdyqNYqUms1jo/ie81J7b9Nh60\n2yxJbB7wzi05zo0qVfJg/PjJrFq1jCVLFlKwYCFGjx6Pl5e3SV0fnw+4f/8eK1cuIyYmmqJFi+Pv\nP8uwC/CAAUOYN28Ww4YNQKVSUb9+Y/r3/x8AzZu3ZP/+vfTs2YXt23cbtduv30D0ej1jx45Eo9FQ\no4Yn8+YFpLsc+W14mfsAULt2HSZOHItOp3upnY5jYu7y0UfNgZSl3wULFsTH5wO6dv00w3Pt7Ozo\n06c/3347hWPHjr52wm9jY8vMmXOZOdOf7du3UbJkKWbOnGd4lnfPnl34+0/k8OGTADRp0oyoqEhm\nzfHmXuAAACAASURBVPoWjSaJ+vUbMWBAyvg6ODgye/YCFiyYw+efd8PGxpY2bdrTvXsvQ3+pM8Dt\n2nV8rbiFEEIIIbKKYdOnu+dQxN9Nd4mx1qYQ93ueyI4QxVuk0L/qg445hEajJTY2PuOKeVjfvp/j\n6VmbXr2+yO5QXpujo/UbG0+dTkfXrh0YNmwUNWp4vpE287pTp04yc6Y/69dvemOvO3qTYyqyn4xn\n3iLjmbfIeOY9MqYZS2vTJzB+DU+q7H4dj4znq8uf3y7dY7LkOBdLSkriwoVzhIXdxMUlf3aHk+Mo\nlUq6d+/J1q2bszuUXGPr1s188kmPN5bMCiGEEEK8LWZRIdj92d8wI/vsl2GJsbktOkvnbE9mxdsj\nS45zsatXrzBwYB9Kly5LgwaNMj7hHeTr24odO37l8uVLlC5dJrvDydGuXg0lOvqOyS7SQgghhBA5\njfr8euz2j+LZedicNiMrsoYsOf7/9u48Pqrq8Pv4dzIzmWRISJQdtSgqgoQ1D8aCC+IKaItYf7Zl\nUSn9KVJ42mLZFDdKwKVWwUJxoy3kqWJRrEot1N2i0g6bBlLFIosaDEiELGSSmfP8ETPMmgyQzJbP\n+/XiRebcZc69J5eZL+fec5BQuBUj9dCmqYX2TC20Z2qhPVMPbRqec32hnJsWSwqdU9Zrdcjb9juq\n7jdRNb3HxKV+kdCex6+xW47poQUAAACQ8GylLrV5c5bsB7ZJOhpmfb1zFqsOjVrJyMWtDIEWAAAA\nQMKylbrkXF+o9C8/8JUFh9naLgWqHDybMNsKEWgBAAAAJCRbqUu5L1wneeskhemVlUWHhy5IuNuL\nETsEWgAAAAAJKaPkL5K3LuRZWUmqa99bFRcX0ivbyhFoAQAAACQcR3GRMopXSAodwfhIj9GquHxh\n7CuFhEOgBQAAAJBQbKUuZb81S5IJuM3Ym9FOledP5xZj+BBoAQAAACQEW6lLmRuXyL73Hcl4A5+Z\ntVh1aOTT3GKMAARaAAAAAHFnK3Up9/nRkvEElNffbmzRYZ6XRRgEWgAAAABxl1HyF8l4wg4AxUjG\niIRACwAAACDu0r7+2Pez/yBQVQNuI8wiIgItAAAAgLhoeGbWts8la1VZwDJveltVDr6DMItGEWgB\nAAAAxFy4Z2YtauidTdOha5bzzCyaRKAFAAAAEFPO9YXK2PpUyDOzDWrOuIIwi6gQaAEAAADETNa6\nqcr4+PmAMv9nZmWxqnrgpJjWCcmLQAsAAACgxfiek93/keSukLWmXJIC55iV5HF2UF2nfFUPnETv\nLKJGoAUAAADQImylLuW+cJ3krQsoP/qsbL2qAbepavDsmNYNqYFACwAAAKBF2D9/T/LWhX1OVpK8\n9jaqHHIXIxnjuBFoAQAAADQ7W6lL6Z/8VVLQM7INLFYd+t7/4/ZinBACLQAAAIBmFTwlT8Mtxl6b\nUybzZNW1z+NZWTQLAi0AAACAZpW5cUnAlDxGkixpOvT9PxNi0azS4l0BAAAAAKnDVuqSY+da3+uG\n242r+t9KmEWzo4cWAAAAQLNwFBepzfsPSPIGDATl7lLAKMZoEQRaAAAAACfMub5Qzk2Lfa99A0FZ\nrIRZtBgCLQAAAIATYit1yblpiSQF9MwaSdXn/phbjdFiCLQAAAAATkib9YWSjG80Y580u2p6/iA+\nlUKrQKAFAAAAcFwcxUVybnhI1qqygHKvzana0y5iah60OAItAAAAgGPmKC5S9pszfK+P9s5amJ4H\nMUOgBQAAAHDMHJ+ukaTAuWYlVQ2gVxaxwzy0AAAAAI5ZzZkjJNUH2YYwe6THaEY0RkzRQwsAAAAg\narZSlzI3LpFtn8uv1KKqAZMIs4g5Ai0AAACAJtlKXXKuL1T6lx8ElDc8O2scbeNSL7RuBFoAAAAA\njXKuL5Rz02Lf6+C5ZmWxqPaU78a6WgCBFgAAAEBkjuIiX5gNCbLfqup/KwNBIS4ItAAAAADCspW6\n1Gb9PEmhoxlLkif7VFXlT1FN7zExrxsgEWgBAAAAhFE/z+xM+UfYhp9quxSocvBsemURdwRaAAAA\nAD7Bgz8F98weHno/PbJIGARaAAAAAJLqw2zu86Ml45EUGmarBtxGmEVCSYvlm7ndbs2ZM0eDBg3S\nkCFD9MQTT0Rc99NPP9X48ePVr18/XXnllfr73/8ew5oCAAAArU+b9YWS8cii8GGWeWaRaGIaaB94\n4AFt3rxZy5Yt07333qslS5bolVdeCVmvsrJSN998szp37qwXX3xRY8aM0bRp07Rjx45YVhcAAABo\nNRzFRbL7zTFr1BBmLTo89H7CLBJSzG45rqqq0sqVK/X73/9eeXl5ysvL08SJE7VixQqNHDkyYN3V\nq1fLZrNp3rx5stvtOv300/XPf/5TmzZt0llnnRWrKgMAAACthtO1SFJ9zyyDPyFZxCzQlpSUyO12\nKz//6MWQn5+vxYsXy+PxyGq1+so/+OADDRs2THa73Ve2dOnSWFUVAAAAaFWc6wtlPbxXErcYI7nE\n7JbjsrIy5eTkyOFw+Mrat2+v2tpaHThwIGDd3bt3q127drrnnnt0wQUX6Nprr9Ubb7wRq6oCAAAA\nrYKt1KXsNROVubm+86jhuVlPRjvCLJJCzAJtdXW10tPTA8oaXrvd7oDyyspKPfXUU2rbtq0ef/xx\nDR8+XJMnT9ZHH30Uq+oCAAAAKc25vlC5q74vx85XZfl2VOOG3tmaXjfEr2LAMYjZLccOhyMkuDa8\nzszMDCi3Wq3q0aOHfvnLX0qSzj33XLlcLq1cuVJ5eXlB61qUm+tswZojlqzWNNozxdCmqYX2TC20\nZ2qhPVNPS7SpZeMflLbh99LhL2VxH64v81tuJJm2pyl9xK+VHnYPOF5coy0jZoG2U6dOOnTokNxu\nt69ntqysTOnp6crJyQlYt2PHjvrOd74TUHbGGWeEHeXY4zEqL69quYojpnJznbRniqFNUwvtmVpo\nz9RCe6ae5m7TrHVTlfHx8wFlwVPzSFLFwJ+pht+lZsc1evw6dMiOuCxmgbZXr16y2+3atGmTCgoK\nJEkul0u9e/eWzRZYjQEDBuidd94JKNuxY4dOOeWUWFUXAAAASFq2UpcyNy6Rbf9HkqdGqq2WtbZC\nUmiPbANP9qmqyp+imt5jYlpX4ETE7BnazMxMjRo1Svfee6+2bt2q1157TU8//bTGjx8vqb639siR\nI5KkG264QTt37tSDDz6o3bt36w9/+IPee+893XAD9/IDAAAAjbGVupS76lo5dr4q6+G9slaVhYTZ\no3PMSnXte6v8uhd1cPz7hFkknZgFWkmaNWuW+vTpoxtvvFF33323Jk+erBEjRkiSLrjgAq1Zs0aS\n1LVrVy1btkwffPCBRo4cqZUrV2rhwoU699xzY1ldAAAAIGk0jFjcdvUNkryySCF//IOsJ/tUHR56\nv8pv+DvzzCJpWYwxpunVEldtrYd70VMIzxakHto0tdCeqYX2TC20Z+o5ljZ1FBcp+82Z8r+J2KLA\nW4ob1LXvrYqLCwmxMcY1evwS4hlaAAAAAM3PP8yGez7Wa8+WsWfIOHJV3W8itxUjpRBoAQAAgCRk\nK3XJub5Q6V9+ICn8iMVHeoxWxeULY143IFaOOdDu27dPO3fuVP/+/VVRUaH27du3RL0AAAAAROBc\nXyjnpsW+18FhlhGL0VpEHWgrKys1a9YsrV27Vmlpafr73/+uwsJCHTx4UL/73e/Url27lqwnAAAA\nANXfYtwQZkNvMbbo8NAFBFm0GlGPcnz//ffr4MGDeu211+RwOCRJM2fOlCT9+te/bpnaAQAAAPCx\nlbrU5v0HJIWbgocwi9Yn6h7a119/XY8//rhOOeUUX1m3bt10zz33+OaSBQAAANAybKUu5T4/WjIe\nSYHPyjJyMVqrqAPtkSNHZLfbQ8rdbreSfOYfAAAAIKE5iovUZv08yXgCe2ZtTlVccDe9smi1or7l\n+NJLL9VvfvMbHTp0yFf22Wefae7cuRo6dGhL1A0AAABo1WylLuU8c4Wy35yhNPfR7+EN3UmEWbR2\nUQfaOXPmyG63q6CgQNXV1Ro1apSGDx+u3Nxc3XHHHS1ZRwAAAKDVsWz8g3JXfV/2A9vqX3/7pyHM\nVg24jTCLVi/qW46zsrK0aNEi7dmzR59++qnq6up0xhln6Mwzz2zJ+gEAAACtSsP8stZG5petGnCb\nqgbPjnndgEQTdaDds2eP72f/ENtQftpppzVjtQAAAIDWp6n5Zb3Ojqo8bxo9s8C3og60l19+uSwW\nS0i5xWJRWlqaPvroo2atGAAAANCaND6/rHSkx2hVXL4w5vUCElnUgfa1114LeO3xeLR792499thj\nuvXWW5u9YgAAAEBr4SguUta790riFmPgWEQdaP3nn23wne98Rzk5Obr99tsZ6RgAAAA4DsG3GfsH\n2douBaocPJv5ZYEIog60jdm3b19z7AYAAABoVcLdZmwkeTPaydxQpG+y8uJWNyAZRB1oH3300ZCy\nyspKrV27VkOGDGnWSgEAAACpylbqUubGJbLtc8laVSYp9DbjyvOnK/PU86TyqrjUEUgWUQfaf//7\n3wGvLRaL7Ha7Ro0apZtvvrnZKwYAAACkGkdxkbLfnCn/G4uDw2zD/LKZsa4ckISiDrTLly9vyXoA\nAAAAKc0/zAbPHVIfZi06PHQBU/IAx6DRQPuXv/wl6h394Ac/OOHKAAAAAKnGVuqSc32h0r/8QFL4\nUYwJs8DxaTTQLl68uLHFPhaLhUALAAAA+AkOslJomPU4O6iuU76qB05iJGPgODQaaF9//fVY1QMA\nAABICY0FWelomD089H56ZIETdEzT9pSVlWnnzp3yeDy+MrfbrW3btmnSpEnNXjkAAAAgmQQP+hQu\nyEpHB34CcGKiDrR//vOfNW/ePNXV1clisciYby9Si0X9+vUj0AIAAKBVqw+zMyRFDrKe7FNVlT+F\nMAs0k7RoV3ziiSd06623auvWrWrXrp3eeOMNvfzyy+rVq5cuvfTSlqwjAAAAkNCc6wtDwqzR0TBb\n26VA5de9qIPj3yfMAs0o6kD71VdfadSoUUpPT1fv3r21adMmnXXWWZo9e7aee+65lqwjAAAAkJBs\npS7lPHOFnJvqB1MNHvSpIch+M3oVgz4BLSDqQNuuXTt9/fXXkqTu3btr+/btkqROnTrpq6++apna\nAQAAAAnIUVykk/50vnJXfV/2A9skhYbZqgG3EWSBFhZ1oB0xYoRmzJghl8ulCy+8UKtWrdKaNWu0\ncOFCdevWrSXrCAAAACSMhmdlrYf3SqoPshYF3mJcNeA2VQ2eHacaAq1H1INCTZs2TW3btlV5ebku\nvfRSXX/99brvvvuUm5ur+fPnt2QdAQAAgIRgK3WpzfsPSAo/8FNd+96quLiQXlkgRiymYbjiJuzY\nsUNnnXVWS9fnmNXWelReXhXvaqCZ5OY6ac8UQ5umFtoztdCeqYX2bHnBU/IEa+5eWdo0tdCex69D\nh+yIy6Luof3e976nM888U1dffbVGjBih0047rVkqBwAAACQyW6lLzvWFSv/yA0mhPbN1J52timEP\n0SsLxEHUz9C+9dZbuuGGG/Tuu+/qyiuv1A9+8AMtW7ZM+/bta8n6AQAAAHHjXF+o3FXfDwmzvudl\n0+yEWSCOog60HTp00NixY7V8+XK99dZbuvbaa/XOO+/oiiuu0NixY1uyjgAAAEDMOYqLAqbjCTsl\nz7V/IcwCcRT1Lcf+vF6vvF6vjDGyWCxKT09v7noBAAAAceEoLlLmlidlLd8pKTTIShYdHrpANb3H\nxKF2APxFHWh3796ttWvXau3atSouLlbfvn01cuRIPfjgg2rfvn1L1hEAAACIiax1U5Xx8fMBZf5D\nQNV2KVDl4Nn0ygIJIupAe8UVV+jcc8/V8OHD9cgjj6hr164tWS8AAAAgZmylLrV5c5bsB7ZJCh34\nyWtz6tD3/0yQBRJM1IF2zZo16t69e0vWBQAAAIi54Ol4LAqdmOdIn5sIs0ACijrQ+ofZgQMH6sUX\nX2TqHgAAACQlW6lLmRuXyLbPJWtVmaTQZ2W9Vqdkz9SRXjc06/yyAJrPcQ0KZUz4yaQBAACARBUu\nxDYIDrNHeoxWxeULY1o/AMfuuAItAAAAkExspS7lvnCd5K3zlVmC1mkIs1UDbqNHFkgSxxVo+/bt\nK7vd3tx1AQAAAJpVQ6+sfe87krcuYohtQJgFkkvUgdbj8eiRRx7Rc889p/Lycl1yySVq166dxo4d\nq1tvvbUl6wgAAAAcs3C9suEenPM4cmWcHVTdbyJzywJJJupAW1hYqH/84x+aPn268vLy5PV69eGH\nH2rRokWqra3VlClTWrKeAAAAwDHJKPlLxF5Zb3pb1Z4yWNUDJzF6MZDEog60f/3rX7V48WINGjTI\nV9azZ0+deuqpmjZtGoEWAAAACcNW6lLGtv8nKUyvrMWmQ9csJ8gCKSDqQOt0OmW1WkPKs7OzlZaW\n1qyVAgAAAI6XrdSl7LWTJeMJGL3YWB2q69hflYNnE2aBFBF1oL399tt1xx136Pbbb9eAAQNktVq1\nfft2zZ8/X+PHj9eePXt86zI/LQAAAFqabxqe/R9JnhrJUytLXbXSPDUB6xlJslj1zaiVBFkgxVhM\nlJPK9uzZ8+hGlvr/6/Lf1GKxyBgji8Wi7du3N3M1I6ut9ai8vCpm74eWlZvrpD1TDG2aWmjP1EJ7\nppbW1p62Updynx8tGU/Y5cHzyh4een/SDfjU2to01dGex69Dh+yIy6LuoX3ttdeapTIAAADAicrc\nuCTgluJw/OeVTbYwCyA6UQfaU045pSXrAQAAAETFVuqS47N1vteN3W7IvLJAaovpaE5ut1tz5szR\noEGDNGTIED3xxBNNblNeXq4hQ4bo+eefj0ENAQAAkOjarC/09c42hFmPI1ceR668Nqc8zg6qOeMq\nlV/3ImEWSHFR99A2hwceeECbN2/WsmXLVFpaqunTp6tr164aOXJkxG0KCwu1f//+GNYSAAAAichR\nXCTne/NlrSmXFHhLMcEVaJ1i1kNbVVWllStXatasWcrLy9Nll12miRMnasWKFRG3eeutt7R161ad\nfPLJsaomAAAAElDWuqnKfnOGL8xa5Dfwk6Nt3OoFIL5iFmhLSkrkdruVn390qPT8/Hx9+OGH8nhC\nR6erqKjQPffco7lz58put8eqmgAAAEgwWeumKuPj+sfPGoKsUcN0PDbVnvLd+FUOQFzFLNCWlZUp\nJydHDofDV9a+fXvV1tbqwIEDIes/+OCDuvDCCzVo0KBYVREAAAAJJjjM+oKsJE/OGSofvYq5ZYFW\nLGbP0FZXVys9PT2grOG12+0OKN+wYYPeeOMNvfLKK03u12q1KDfX2XwVRVxZrWm0Z4qhTVML7Zla\naM/Ukmrtadm7QZa//UppX31Y/1pHg6xp00nei2bIDLxJWXGrYctLtTZt7WjPlhGzQOtwOEKCa8Pr\nzMxMX9mRI0d05513as6cOcrOjjyBbgOPxzBBcQphwunUQ5umFtoztdCeqSWV2tNW6lLuqmsleSUF\nhtkjPUar4vKF9S9S5HgjSaU2Be15Ijp0iJwLYxZoO3XqpEOHDsntdvt6ZsvKypSenq6cnBzfelu3\nbtWuXbs0ffp0X1l1dbXuvvtubd68Wffdd1+sqgwAAIAYchQXKXPLk0r7Zpck79FBn779OyDMAoBi\nGGh79eolu92uTZs2qaCgQJLkcrnUu3dv2WxHq9G3b1+tXbs2YNsxY8boxhtv1OjRo2NVXQAAAMSA\nrdSlzI1LZPvifd8Ixv4IswAaE7NAm5mZqVGjRunee+/VggULVFZWpqefflpz586VVN9bm52drYyM\nDHXr1i1g27S0NLVr107t2rWLVXUBAADQwvwHfGpg8fuZMAugKTEb5ViSZs2apT59+ujGG2/U3Xff\nrcmTJ2vEiBGSpAsuuEBr1qyJZXUAAAAQJ23/OiZkKh7/UYwJswCiYTHGmKZXS1y1tR4erk4hPCyf\nemjT1EJ7phbaM7UkQ3s2PCNrqfhS1toKSQp5TlaSvPZsebM6q7rfRNX0HhPzeiaKZGhTRI/2PH4J\nMSgUAAAAWidbqUtt3pwl+4FtAeX+oxdLkif7VFXlT2nVIRbAsSHQAgAAoEU4iovkdC2S9fBeX1m4\nZ2Q9OWfo8GWPqK5zfkzrByD5EWgBAADQrML1yIa7tVjiGVkAJ4ZACwAAgGZhK3XJub5Q6V9+4CsL\n1yPLM7IAmguBFgAAACfMub5Qzk2Lfa/pkQUQCwRaAAAAnBD/+WTpkQUQSwRaAAAAHBNbqUuZG5fI\ntv8j6Uh5o1PwVA24TVWDZ8e8jgBaBwItAAAAouYoLlL2mzMVfDNxcJit7VKgysGzGbkYQIsi0AIA\nACAq/s/JWoKWHY23Fh0euoBbiwHEBIEWAAAATQr3nGzwgE917Xur4uJCemUBxAyBFgAAAI1yri+M\nGGY9jlwZZwcGfAIQFwRaAAAAhPAN/LTPJWtVmaTQMHt46P2EWABxRaAFAACAJL8Q+8X7staUByyz\niOdkASQeAi0AAABkK3Up9/nRkvH4yoIHfpIkT/apOnzF73hOFkBCINACAAC0crZSl7LXTpaMp5HR\niyVZrIRZAAmFQAsAANCKhZtXNnj0Yo+zg+o65at64CTCLICEQqAFAABopWylLmW/NVuSCRnwyWtz\nytOhjyoHzybEAkhYBFoAAIBWxlFcJKdrkdIq9wXcZtwQZqsG3KaqwbPjVT0AiBqBFgAAoBVwFBcp\nc8uTslR8KWttRcAy/1uMCbMAkgmBFgAAIMU51xfKuWlxQFm4wZ+qe48jzAJIKgRaAACAFGYrdcm5\naYmkwBAbPPCT0uyq6fmDWFULAJoFgRYAACBFOYqL1Gb9PDUM+hQcYk1aurwZOYxgDCBpEWgBAABS\njK3UpTZvzpL9wLaQZV6rQ96231F1v4mq6T0mDrUDgOZDoAUAAEgRtlKXnOsLlf7lB76ywBGMLTo0\naiU9sQBSBoEWAAAgBVj2blDuC9dJ3rr6137Ljk7Hw23FAFILgRYAACAFWD58RvLWRRz4iel4AKQi\nAi0AAECSy1o3VWkfPy8pdOCn2i4Fqhw8m55ZACmJQAsAAJBkbKUuZW5cItv+j6SqA7J6qiUFPi9b\nd9LZqhj2EEEWQEoj0AIAACQR5/pCOTctDikPGPzJYiXMAmgVCLQAAABJIHgqHkvQ8qO3Glt0+OJC\nwiyAVoFACwAAkMCanornKE/OGTp82SOEWQCtBoEWAAAgAUUbZD2OXBlnB1nOn6SD3f8npnUEgHgj\n0AIAACSY4Odkw03FU9e+tyr8bi3OzXVK5VWxqyQAJAACLQAAQALJWjdVGd9OwRNpTtkjPUar4vKF\nMa0XACQiAi0AAECCcK4vDAmzAbcXZ5+qqvwpquk9JuZ1A4BERKAFAABIAP63GVsUGGRruxSocvBs\nBnsCgCAEWgAAgDhxFBcpc8uTslR8KWtthaTA24w9zo46PPwJgiwARECgBQAAiCFHcZGcrkWyVO5T\nmrc2YFngbcYWwiwANIFACwAAEAOO4iI5Nzwka1VZQHn4gZ8sOjx0AWEWAJpAoAUAAGgGvtuHa8rr\nCzy1snjcMtZ0yVvnu6VYijx6sRQ6HQ8AIDICLQAAwAmwlbrU5s1Zsh/YFn6FuqNzw4YbuViSvPZs\nebM6q7rfREYwBoBjQKAFAAA4Tv4jE0uBPa/h+AdZk5Yub5uOTMMDACeAQAsAAHAMGhuZOLjnNRyv\ns6Mqz5tGiAWAZkCgBQAAaIKt1KXMjUtk++J9WRuekf1WcJj12rNl7BmBz9Ba7TKOXG4pBoBmRqAF\nAABohK3UpdxV10ry+soiDep0pMdoVVy+MFZVA4BWj0ALAAAQRkOvrH33m5K8jY5MzG3EABAfBFoA\nAAA/tlKXnOsLlf7lByHL/IMsIxMDQPzFNNC63W7NnTtXr776qtLT03XTTTfppz/9adh116xZo8WL\nF2vv3r36zne+o5///OcaNmxYLKsLAABaGUdxkbLfnKmG6BquV5beWABIHDENtA888IA2b96sZcuW\nqbS0VNOnT1fXrl01cuTIgPX+9a9/afr06brrrrtUUFCgt956S1OmTNFzzz2nc889N5ZVBgAArYT/\nFDw8IwsAySEtVm9UVVWllStXatasWcrLy9Nll12miRMnasWKFSHrrl69WldccYX+53/+R926ddP4\n8eNVUFCgNWvWxKq6AACgFXEUF4WEWaOjYdaTfaoOD72fMAsACSZmPbQlJSVyu93Kz8/3leXn52vx\n4sXyeDyyWq2+8nHjxslmC6yaxWLRoUOHYlVdAADQSthKXWqzfp6k0Cl4arsUqHLwbNV1zg+7LQAg\nvmIWaMvKypSTkyOHw+Era9++vWpra3XgwAF17NjRV96zZ8+AbT/55BO99957evjhh2NVXQAAkMIc\nxUXK3PKkLFVlIfPKNoTZw0Pv5zlZAEhwMQu01dXVSk9PDyhreO12uyNud+DAAf3sZz9Tfn6+rrji\nipDlVqtFubnO5q0s4sZqTaM9Uwxtmlpoz9TSWtvTsvoWWYufCyz79m/fLcbDH1bmwJuUGdOanZjW\n2p6pjDZNLbRny4hZoHU4HCHBteF1Zmb4j4vS0lJNmDBBaWlpWrhwodLSQh/59XiMysurmr/CiIvc\nXCftmWJo09RCe6aW1taejuIiOTc8JGtVmaTAgZ+ko2G2asBtqur+P1KSnZvW1p6tAW2aWmjP49eh\nQ3bEZTELtJ06ddKhQ4fkdrt9PbNlZWVKT09XTk5OyPp79uzRjTfeqMzMTP3pT3/SSSedFKuqAgCA\nFOC7rbimXKqtlrW2wrfMosDRixtUDbhNVYNnx6yOAIATE7NA26tXL9ntdm3atEkFBQWSJJfLpd69\ne4cMAFVeXq6bb75Z2dnZWrZsmU4++eRYVRMAAKSAtn8do/Q9b4WUB/fKetPSZdp0VF37PFUP7fvM\ndwAAIABJREFUnMTgTwCQZGIWaDMzMzVq1Cjde++9WrBggcrKyvT0009r7ty5kup7a7Ozs5WRkaHf\n/va3OnjwoBYtWiSPx6OysvpbgzIyMpSdHbm7GQAAwD/MRrqtuF6aDl37HCEWAJKYxRgT7o6bFlFd\nXa177rlHa9euVZs2bTRhwgRNmDBBknTOOedo/vz5Gj16tAoKClReXh6y/TXXXKOHHnoooKy21sO9\n6CmEZwtSD22aWmjP1JJq7RnuGdlwX3K8jpNU27Ug5XpkU609QZumGtrz+DX2DG1MA21LINCmFi70\n1EObphbaM7Uke3s61xfKsf1ZyXgkb13EZ2S9VqeMo42MI1fV/Sam7FQ8yd6eCEWbphba8/glxKBQ\nAAAAzcFW6lLWuv8r26HPQpYFT7/jPu1iHfpeUayqBgCIMQItAABICrZSl5zrC5X+5Qe+ssaekT3S\nY7QqLl8Yk7oBAOKDQAsAABKac32hHMUrZHUf8pUF98T68zo7qvK8aSl7azEA4CgCLQAASCi2Upcy\nNy6Rbf9HUtUBWT3VvmX+PbKt7RlZAEAoAi0AAEgYzvWFcm5aHFIeLshKUtWA21Q1eHaL1wsAkJgI\ntAAAIO7CTbnjL/jW4touBaocPDulpt0BABw7Ai0AAIirrHVTlfHx877XkZ6P9dqzVXvqkJSbPxYA\ncPwItAAAIG78w2y4IOtx5Mo4O/BsLAAgLAItAACIKd+gT1+8L2tNuaTQMFvXvrcqLi6kJxYA0CgC\nLQAAiJlwgz5ZxEBPAIDjQ6AFAAAtxlFcpMwtT8pSUy7VVPim4Ak3ajG9sgCAY0WgBQAAzcZRXCSn\na5Es1V/LGBMwh2yDcM/KHukxWhWXL4xJHQEAqYNACwAAmkXwaMUNGpuCx5N9qqrypzDgEwDguBBo\nAQDACQs3WnGD4Ol3JMmTc4YOX/YItxcDAE4IgRYAAByXSKMVhwuwXnu2TJpVFotVR3rdwKBPAIBm\nQaAFAADHxFbqUps3Z8l+YFtAuX+YNWnp8tqdzCELAGhRBFoAABCVhgGfrIf3+srCjVbMAE8AgFgh\n0AIAgEaF65ENF2QlwiwAILYItAAAIKxjCbKMVgwAiAcCLQAACGArdcm5vlDpX37gKws3d6wk1bXv\nrYqLCxmtGAAQFwRaAABaOUdxkTK3PClLTbnkqfWNWCyF75H1Ok5SbdcCVQ+cRJAFAMQVgRYAgFbI\nUVwk64dP6aRDX8haWxGyPNKtxVUDbmPKHQBAwiDQAgDQigQ/F2v9ttwSZl3/IMutxQCARESgBQCg\nlXCuL5Rz02Lf6+AQG/x8rCTVdilQ5eDZBFkAQEIi0AIAkOIcxUVyvjff92xspAGeJMnjyJXSs1TX\nPo9nZAEACY9ACwBAirGVupS5cYls+z+SjpQHPCMbHGa99myZjBwCLAAgKRFoAQBIAb6Riiu+bHSQ\np4C5Y8+fqoP502NSPwAAWgKBFgCAJBQw1U5tdUiIbez5WE/OGTp82SPK6nmhVF7V4nUFAKClEGgB\nAEgSvhB7aK+snuqQ5U0N8mTsbVSddyPT7gAAUgaBFgCABOcoLpJzw0OyVpUFlEczSrHHkSvj7KDq\nfhNV03tMi9URAIB4INACAJCAfAM7ffG+b3RiKTDEhguwXqtTstrkadeL6XYAACmPQAsAQAJxFBfJ\n6Vok6+G9AeWRptrxWp0yjjYyjlx6YQEArQ6BFgCAOGpscKdIvbFeq1OyZ+pIrxt4HhYA0KoRaAEA\niLFItxM3iBhknR1Ved40emEBAPgWgRYAgOMQ0LMqSZ5aWTxuGWu6ZLUHruy/TAoJsU0N7kSQBQAg\nPAItAAB+Gp5htVR/HTmc1lYqzVsbfgd1jczr6resyRBrz5Y3qzPPxQIA0AgCLQCg1fDd6rv/I8ld\nEdqjGvQMa6PhVKGh9FgFh1im2AEA4NgQaAEAKckXXve5ZHFXyljSAsNqgzChNdqgGm7anGPlcXZQ\nXad8VQ+cxBQ7AAAcIwItACBp+Z5jrSoL7G311IYdbEmKLqweS1D12rNl7BnRP0NrtUtWh+ra5xFi\nAQA4QQRaAEDScRQXybnhIVmrygIXBPW2Rgqv0QRWk5Yur90ZOZxmnqyq/CncGgwAQBwRaAEACSni\nKMLGyOqp9q3XVI9rpPDqsWfJYrwhParGkcszrAAAJAkCLQAg7kIGa2psFOFvNQTZaHpbvVaHjC1T\nSs/iVl8AAFIIgRYAEDMhAzVFmJe1QWO9r/5B1mt1ShYF9rbynCoAACmPQAsAaFYBtwr7D4akMMG1\nkXlZpaZ7X73Ojqo8bxq3BwMA0EoRaAEAxyTis63WdMlb1+TUONH2ujYIN4owc7UCAACJQAsAUCPT\n3zT4NkxaJGWHmbdVUtShVWq859U3WBOjCAMAgCYQaAEghQUMtuSpqS8MnhO1tjq0VzVSaP3WiQTW\nBr6BmpiXFQAAHKeYBlq32625c+fq1VdfVXp6um666Sb99Kc/DbtuSUmJ7r77bpWUlOjMM8/UPffc\no759+8ayugCQMMIGUyk0nPqX11UrzX/dYFHO2RpOVIHVni2TZg2sH8EVAAA0o5gG2gceeECbN2/W\nsmXLVFpaqunTp6tr164aOXJkwHpVVVWaOHGiRowYocLCQj3zzDO65ZZbtG7dOmVlZcWyygDQrI45\nmErhe1CDNdKjGm1QjSak+gv3bKusduZxBQAAMROzQFtVVaWVK1fq97//vfLy8pSXl6eJEydqxYoV\nIYF2zZo1stvtmjlzptLS0jR79my99dZb+tvf/qbrr78+VlUGgLAcxUVyuhbJUv11+ADaSK9ppOlp\nfJohmIYTbVgNO/2NdPQZWptDnsz2BFYAABKUMcb/Rfi/m1xuAv4KXm4U7j2CXivCvo91PUnqkK1I\nYhZoS0pK5Ha7lZ9/9Baz/Px8LV68WB6PR1ar1Ve+ZcsWDRw4UGlpaZIki8WigQMHatOmTSGB9rYi\nlz76/JAq3XWq8xjZrBbZrWkB69R6vGGXRSpv7m3Y3zHszxjZLCl2TEm4v+asgyXNInetJ+7HFM3+\nxjne1C3pr6qtpfLoRk09b9rYs6YRljU6yq//P97BnzkNRWH+nQ8ssIQt9zhy6ntQ64KOyUjGkaMj\nvcepptf1gRUJ+lDJaZuhb76pri84+HXE9Uw0H04R14nyw9b3oRphvYCfG/9gjna5CXccEfd1fB/c\nIecu0rGF3cexnbvKNg7VVNY0sa9oz10Tx6sw56+Zz53/C3O85yaa5U397p3ouQuzTsRz57cPj8Ou\nI0fcTa4XeowKWKfZzl3YdYJ+30/03IU7jgjHbJo6JxHOR/h1wh9z6PEE7aup/QQtq7RZVVdbF7SP\nhsXHev1Es074+kV97vz3Ge35DXmvJn7fwtYj0rqNLzeR1mt0X9Geu8DlRkYHgo/veH9Povk9SjGd\nSrZHXBazQFtWVqacnBw5HA5fWfv27VVbW6sDBw6oY8eOAeueccYZAdu3a9dOJSUlIfs98Pa7OtXr\nlWR8X+Es3zaqRUaWb9vX8u0viMUY38/+6wSUGeP3dfDocovfL7ul4f187xWu7OhrX9386hSuzv71\nCleXhvcK3ubo+oGvg89HwHsE1cvvLY7uz/99Q5YFng//ugWfF//twx9HUFmYbRo7V6HHHc35aHqZ\nwrRDo+cqwr7DtpXvLU7wXAW8Z+TfkYb3PuFzFfZ3xH8/J3qu/I8j8vmI7lwF7zvy77VDtcq01KjM\nSGU6+h9sklUyGX6vs2SUFfih5//O/p9TIT8EMf41jiW7JGdQWZWkpd/+iezrFqoR4uNQvCuAE2Ox\nBPxdbbE0utz3t/+/O5bw61oUvG3QetEuD1uPwDe3RKpn0HqRlze2TuA5ifheinAcAds3tU7QOYxy\nvYDjCFrHeK0ydd7Gz7/F4ndcwXUNcxzH+nti+fYn33GFP7dh3yPq89vEuQs+nnD7avL3JPzyiOcu\n7L6iPXfhl2dk2FVTUxf0HhH21dQ11sR6loC6HOv1HO01ePQ9jq4a3bV4zMsbEbNAW11drfT09ICy\nhtdutzuqdYPXk6R5659o5pq2Xt6Ar/ny/QLVR6jAX2wTtK7x+2XzLQtXFmb9huUm6KIJXjcg2luC\n6uq3ffB7+fZnCYjFvrKQbYL27X/skY/jRM+V33FEqJv/sYccW8MfS1rgMssxnqtwbR5U74D/EjrR\ncxXm96DJZSd6rvzW9V92mdWlHEuV/6qBP4b79zSkzISUN7p9Qw+pRZLXIxmPZLFK396d4tvGeGUx\nRiarg9R9mEzOqaEfyE19GEX4AAz9cud/fKEfdGlpFnkbDvM4PxgDPmSj/TBt8jgj1aWxY42u/hHP\nUaNfFoL3Ed16Iecqmi9WEeofzfFa09Lk8fr9j0tTx9zkcTaxfqPbRvceEc9RFO/R9HE2vn7Al7fj\n/LLWZBho5DgswdsEsVrT5PF4G10HyYU2TS20Z8uIWaB1OBwhgbThdWZmZlTrZmRkKNj/vXhq/Re9\noNBz9IusJeALta/M70vt0WUN+5BksQR+SbcEfgEOLDv64dqwTnBoqX8/S9DroJAW5gt7Q33ChZLA\nY/IvC9rft8fjv70J9wUAaMUOprXVfPtT9S+ivCxMWrq8dmf0z9A2bHecgyYFxa24aJvrVHl5ffCP\n1PHclOPdDs0vN9epyvLGp2hqlYJ/SY/rl9YE/d3ycv2uT6QG2jS10J7Hr0MiPEPbqVMnHTp0SG63\n29f7WlZWpvT0dOXk5ISsW1ZWFlC2f/9+dejQIWS/3b6bn/LP0NoTvH48Q5t6+2uNz9D+1XO5cmz2\npp+h1fEHUgAAADSvmAXaXr16yW63a9OmTSooKJAkuVwu9e7dWzZbYDX69eunJUuWyBgji8UiY4w2\nbtwYds7axWPy+Z+OFML/XKWe5GrTC+TRnToY72oAAAAgKmlNr9I8MjMzNWrUKN17773aunWrXnvt\nNT399NMaP368pPre2iNHjkiSrrrqKlVVVWnu3LnasWOH5s+fr8rKSo0YMSJW1QUAAAAAJLiYBVpJ\nmjVrlvr06aMbb7xRd999tyZPnuwLqRdccIHWrFkjScrKytLSpUu1adMmXXvttdq4caMef/xxZWVl\nxbK6AAAAAIAEZjEhkz0ll9paTxLdzoimJNftqYgGbZpaaM/UQnumFtoz9dCmqYX2PH6NDQoV0x5a\nAAAAAACaC4EWAAAAAJCUCLQAAAAAgKREoAUAAAAAJCUCLQAAAAAgKRFoAQAAAABJiUALAAAAAEhK\nBFoAAAAAQFKyGGNMvCsBAAAAAMCxoocWAAAAAJCUCLQAAAAAgKREoAUAAAAAJKWkDbRut1tz5szR\noEGDNGTIED3xxBPxrhIasXv3bt16660aNGiQLrroIi1YsEA1NTWSpLvuukvnnHNOwJ8//OEPvm3f\nf/99XXPNNerXr5/GjRunXbt2xeko4O/ll18OabfbbrtNkvT5559rwoQJ6t+/v4YPH6633norYFva\nNLE8//zzIW3Z8OeLL77gGk0ibrdbV199tdavX+8rKy8v19SpUzVw4EANGzZML7zwQsA2JSUluuGG\nG9SvXz+NHj1aW7duDVi+Zs0aXX755erXr58mTZqkAwcOxORYEL49i4uLNW7cOA0YMEDDhg3T0qVL\n5fV6fct/8pOfhFyv//jHP3zLac/4CtemS5cuDWmzefPm+ZZzjSau4PZctGhR2M/Snj17+rbhGm0B\nJknNnTvXXH311ebDDz8069atMwMGDDAvv/xyvKuFMGpqaszw4cPNlClTzI4dO8wHH3xgLr30UjN/\n/nxjjDE//OEPzZNPPmm++uor35+qqipjjDFffPGF6d+/v3n88cfNJ598Yn7+85+bESNGGI/HE89D\ngjHm4YcfNpMnTw5ot2+++cZ4vV7zve99z/ziF78wn3zyiVm6dKnp27ev2b17tzGGNk1E1dXVAe1Y\nWlpqRo0aZaZMmWKM4RpNFkeOHDGTJ082PXr0MP/85z995bfccosZN26cKSkpMc8995zJy8szLpfL\nGGNMZWWlGTJkiJk3b57ZsWOH+fWvf23OP/98c/jwYWOMMVu2bDF9+vQxq1atMtu3bzdjx441EyZM\niMvxtTbh2vPgwYPmu9/9rrnvvvvMf//7X/P666+b8847z/zpT3/ybXfhhReaV155JeB6rampMcbQ\nnvEW6RqdNm2amTt3bkCbNVyDXKOJK1x7VlRUBLTj7t27zUUXXWQWLFjg245rtPklZaCtrKw0ffr0\nCfjH4He/+5354Q9/GMdaIZJ//etfpnfv3qaiosJX9te//tUMHjzYGGPMeeedZ95///2w2z7yyCMB\n7VpVVWUGDBgQ0PaIj8mTJ5uFCxeGlK9fv9706dPH92FrjDE33nijefjhh40xtGkyWL58uSkoKDDl\n5eXGGK7RZPDJJ5+Y733ve+aaa64J+HK1a9cu06NHD/PZZ5/51p09e7aZNm2aMcaY5557zgwdOtT3\nHxBer9dcfvnlZuXKlcYYY371q1/51jWm/j8wgveH5hepPVevXm0uvPDCgP8wWrJkibn++uuNMcYc\nPnzY9OjRw+zZsyfsfmnP+InUpsYYM2rUKLNq1aqw23GNJqbG2tPfggULzPDhw43b7TbGcI22lKS8\n5bikpERut1v5+fm+svz8fH344YfyeDxxrBnC6d69ux5//HG1adPGV2axWHTo0CGVlZWpvLxcZ5xx\nRthtt2zZokGDBvleZ2Zmqnfv3tq0aVOL1xuN27FjR9h227Jli84991xlZWX5yvLz87V582bfcto0\ncVVUVOixxx7T1KlTlZOTwzWaJDZs2KCCggI9++yzAeVbtmxRhw4d1K1bN19Z8PU4cOBApaXVfx2w\nWCwaOHCgr/2C27dLly465ZRTaN8WFqk9zzvvPD388MO+9pKOfp5K9f8uOxwOde3aNex+ac/4idSm\nXq9XO3fubPTfWK7RxBOpPf3t3btXy5cv14wZM2S32yVxjbYUW7wrcDzKysqUk5Mjh8PhK2vfvr1q\na2t14MABdezYMY61Q7CTTz5ZgwcP9r32er1asWKFBg8erB07dshms+nRRx/V22+/rZNOOkk33XST\nRo8eLam+rYPbs127dtq3b19MjwGB3G639uzZozfeeEOPPvqojDG66qqrNHXq1IhtVlpaKok2TXTP\nPvus0tPTdf3110sS12iS+PGPfxy2PJrrMfiLdLt27VRSUiJJ+uqrr2jfOIjUnl26dFGXLl18r48c\nOaKVK1fq4osvllR/vbZt21a/+MUv5HK51LlzZ02ZMsW3nPaMn0ht+vnnn6u6ulorV67UL3/5S2Vk\nZOi6667ThAkTlJaWxjWaoCK1p7+nn35avXr18l1/EtdoS0nKQFtdXa309PSAsobXbrc7HlXCMZg/\nf762b9+uv/zlL9qwYYMkqWfPnho3bpw2bNigu+66S5mZmRo+fHjEtqad42vXrl2qq6uT0+nUokWL\ntHv3bs2bN0+VlZWqqanx/U9kg/T0dNXW1kqKfP3SpvFnjNGzzz6rsWPH+trwv//9rySu0WQVqX1q\na2tljGmy/Y4cOUL7JiiPx6Nf/epXqq6u1qRJkyRJn376qSorKzVs2DBNmjRJ69at06233qpnnnlG\n/fr1oz0T0KeffipJ6tSpk37/+99r27ZtvgGhJk6cyDWapKqqqvTiiy9q7ty5AeVcoy0jKQOtw+EI\nadiG15mZmfGoEqJgjNG8efP05z//WY8++qjOPvtsnXXWWRo5cqRyc3Ml1X9p3rVrl/785z9r+PDh\nEdu6YX3Ex9lnn633339fJ510kqT6djPGaNq0abr++utVUVERsL7b7VZGRoakyNcvbRp/xcXF2r17\nt77//e/7yn784x9zjSaxSO2TkZEhi8XS6PKmtkf8uN1u3X777Xr33Xf1hz/8QR06dJAk3X777Zo0\naZLatm0rqf56LS4u9n1Zpj0Tz9ChQwM+T8855xwdPHhQRUVFmjhxItdoknrnnXdkjNFll10WUM41\n2jKS8hnaTp066dChQwENXlZWpvT0dOXk5MSxZojE6/Vq9uzZeuaZZ/Tb3/7Wd4FbLJaQL77du3f3\n3VrRqVMnlZWVBSzfv3+/78Mb8dPw4dvgzDPPVG1trTp27Nhom9Gmievtt99Wv3791KlTJ18Z12hy\n69Spk/bv3x9QdizXY1PbI/aOHDmiSZMm6Z///KeefPJJ9evXz7fMarX6vig36N69u7766itJtGei\nCvd56t9mXKPJ5+2339bQoUNDelu5RltGUgbaXr16yW63Bzwg7XK51Lt3b9lsSdnpnPIWLFigl156\nSYsWLdIVV1wRUH7LLbcErLt9+3Z1795dktSvXz9t3LjRt6y6ulrbtm1T//79Y1NxhLV27VoNHjw4\n4D+Vtm3bprZt26p///4qKSlRVVWVb5nL5fK1GW2auIIHo5C4RpNd//79tW/fPu3du9dX5nK5fCGo\nX79+2rRpk4wxkurvpNm4cWPA9epyuXzbfvnll/riiy9o3zi6/fbbtXXrVi1btixgcExJmjp1qu65\n556Asu3bt/uewaQ9E88f//hHXXPNNQFl27ZtC2gzrtHkE+7zVOIabSlJGWgzMzM1atQo3Xvvvdq6\ndatee+01Pf300xo/fny8q4YwNm/erD/+8Y+aOnWq8vLyVFZW5vtzySWX6O2339af/vQn7d69WytW\nrNDq1av1k5/8RJJ03XXXacuWLVqyZIl27NihO+64Q127dtV3v/vdOB9V6zZo0CAZY3TXXXdp586d\nevPNN/XAAw/oJz/5ic477zx17dpVM2fO1CeffKLHH39cW7Zs8Q0yRJsmrk8++URnnXVWQBnXaHI7\n7bTTdMEFF2jGjBkqKSnRqlWr9NJLL2ns2LGSpKuuukpVVVWaO3euduzYofnz56uyslIjRoyQJP3o\nRz/Syy+/rJUrV+o///mPZsyYoYsuukinn356HI+q9VqzZo3WrVunOXPmqEuXLr7P0q+//lqSNGzY\nMF8bf/bZZ1q4cKFcLpfv+xHtmXguvPBC7dq1S7/5zW+0a9cuvfTSS3riiSf005/+VBLXaDKqq6vT\nzp07dfbZZ4cs4xptIXGaLuiEVVVVmenTp5v+/fubIUOGmKeeeireVUIECxYsMD169Aj7p7a21rzy\nyitm5MiRJi8vzwwfPtz8/e9/D9j+zTffNFdeeaXp27evGTdunNm1a1ecjgT+iouLzdixY03//v3N\nBRdcYBYtWmS8Xq8xxpjPPvvMjBkzxuTl5ZkRI0aYd955J2Bb2jQx9enTx7zxxhsh5VyjySV4TsT9\n+/ebW265xfTp08dccsklZvXq1QHrb9myxYwaNcrk5eWZ6667znz44YcBy59//nkzdOhQ079/f3Pb\nbbeZAwcOxOQ4UM+/PadMmRL2s/TCCy/0rb98+XJz2WWXmby8PDN69GizYcOGgP3RnvEXfI2uX7/e\njB492vTt29cMGzbMFBUVBazPNZrYgtuzrKzM9OjRw3z88cdh1+cabX4WY769hwEAAAAAgCSSlLcc\nAwAAAABAoAUAAAAAJCUCLQAAAAAgKRFoAQAAAABJiUALAAAAAEhKBFoAAAAAQFIi0AIAcAKGDRum\nc845J+TP1Vdf3Sz73759u/797383y76itXz5cs2ZM0eStGnTJl177bUxfX8AAKJli3cFAABIdjNn\nzgwJsDZb83zETp48WZMmTdL/+T//p1n2F43i4mL179/f93Pv3r1j9t4AABwLAi0AACcoKytLHTp0\niHc1mk1xcbF+/OMf+37u06dPnGsEAEB4BFoAAFrYs88+q8cff1xff/21evbsqVmzZqlv376SpK++\n+kq//vWv9d5776m6ulpnnXWW7rjjDg0aNEjjxo3T559/rjvvvFMul0vXXnutxo8fr+LiYl8P8MyZ\nM1VXV6eHHnpIixYtUnFxsSorK1VSUqLf/OY3Ov/88/Xggw/qpZdekjFG559/vubMmaP27duH1POc\nc87x/Xz99df7fn7++ed14MABTZkypYXPFAAAx4ZnaAEAaEGvv/66Hn30Uc2aNUsvvPCCLrroIt14\n44366quvJEnTp09XXV2dnnnmGa1evVqdO3fW3XffLUlatGiROnfurJkzZ+qOO+6I6v3eeOMNXXnl\nlVq+fLkGDhyohx9+WJs3b9bSpUu1fPlyGWN0yy23yBgTsu27776rJ598Ut27d9e7776rN954Qzab\nTa+99pomTJjQfCcFAIBmQqAFAOAE3XfffRowYEDAnwMHDkiSnnzySf3v//6vLrvsMp1++umaNGmS\n8vLy9Nxzz0mSLrnkEs2ZM0dnnnmmzjrrLI0ZM0affvqpjDHKzc2V1WpVVlaWsrOzo6pLbm6uxo4d\nq549e8pqtWrFihW699571a9fP/Xo0UMPPPCAduzYIZfLFbJthw4ddODAAfXs2VMdOnTQoUOH1LVr\nV5166qlq06ZN850wAACaCbccAwBwgn72s5/pqquuCijLzc2VJH366ad6+OGH9eijj/qWud1ude7c\nWZL0ox/9SGvWrNHGjRu1c+dOffTRR5Ikj8dzXANLnXLKKb6f9+zZo9raWo0ZMyZgnZqaGu3cuTPs\nQFM7duzQ2WefLUn6z3/+4/sZAIBERKAFAOAEnXzyyerWrVvYZR6PRzNmzNAFF1wQUO50OuX1ejVh\nwgR98803GjFihIYNG6ba2lr97Gc/C7svi8USUlZXVxfw2uFwBLy3VD8NT3AP78knnxyyrwEDBsjt\ndistLU1PPPGEamtrZYzRgAEDdMstt+jWW28NWy8AAOKFQAsAQAs644wzVFpaGhB47777bp133nk6\n++yz9a9//UvvvPOOOnbsKEkqKiqSpLDPuNrtdklSZWWlcnJyJEl79+7VqaeeGva9TzvtNFmtVh08\neFB5eXmSpMOHD+tXv/qVfv7zn6tnz54B669evVqjR4/Wk08+qZNPPlnTp0/Xddddp4K68qPZAAAB\noUlEQVSCAt/7AQCQSHiGFgCAFnTzzTdr+fLleuGFF7R792499thjWrVqlbp37662bdsqLS1Na9as\n0eeff65XX31VixYtklR/W7IktWnTRv/9739VXl6us88+WxkZGVq6dKn27NmjZcuWadu2bRHfOysr\nS9dff73mzp2r9957T59++qlmzJihjz/+WKeffnrI+larVZmZmRowYIC6deumnTt3aujQoerWrZvv\nFmoAABIJgRYAgBY0YsQITZs2TY899phGjhypdevW6Xe/+5169eqlzp0765577tGyZcs0cuRILV26\nVHfeeafsdru2b98uSRozZoyeeeYZ3XnnncrKytLcuXP1t7/9TVdffbU++ugjjR8/vtH3nzlzpoYM\nGaJf/OIX+sEPfqCamho99dRTysjICFn3ww8/9PXk7tmzR5mZmb6eYwAAEpHFhLunCQAAAACABEcP\nLQAAAAAgKRFoAQAAAABJiUALAAAAAEhKBFoAAAAAQFIi0AIAAAAAkhKBFgAAAACQlAi0AAAAAICk\nRKAFAAAAACQlAi0AAAAAICn9f6rc3gHZwRKgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1118d1c10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pvalues_mann.index = pd.Series(range(0, len(df_pvalues_mann.index)))\n",
    "\n",
    "df_pvalues_mann.p_value.where(df_pvalues_mann.relevant)\\\n",
    "    .plot(style=\".\", label=\"relevant features\")\n",
    "\n",
    "df_pvalues_mann.p_value.where(~df_pvalues_mann.relevant & (df_pvalues_mann.type != \"const\"))\\\n",
    "    .plot(style=\".\", label=\"irrelevant features\")\n",
    "\n",
    "df_pvalues_mann.p_value.fillna(1).where(df_pvalues_mann.type == \"const\")\\\n",
    "    .plot(style=\".\", label=\"irrelevant (constant) features\")\n",
    "\n",
    "plt.plot(rejection_line_mann, label=\"rejection line (FDR = \" + str(FDR_LEVEL) + \")\")\n",
    "plt.xlabel(\"Feature #\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Mann-Whitney-U\")\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Kolmogorov-Smirnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TUzOmTp34mqMqhBBCCCFE9jMMCYBEDQq0kKjJNe+fTSYztJlgEB6I1fbPQKMG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\nAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119d2b610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_pvalues_smir.index = pd.Series(range(0, len(df_pvalues_smir.index)))\n",
    "\n",
    "df_pvalues_smir.p_value.where(df_pvalues_smir.relevant)\\\n",
    "    .plot(style=\".\", label=\"relevant features\")\n",
    "\n",
    "df_pvalues_smir.p_value.where(~df_pvalues_smir.relevant & (df_pvalues_smir.type != \"const\"))\\\n",
    "    .plot(style=\".\", label=\"irrelevant features\")\n",
    "\n",
    "df_pvalues_smir.p_value.fillna(1).where(df_pvalues_smir.type == \"const\")\\\n",
    "    .plot(style=\".\", label=\"irrelevant (constant) features\")\n",
    "\n",
    "plt.plot(rejection_line_smir, label=\"rejection line (FDR = \" + str(FDR_LEVEL) + \")\")\n",
    "plt.xlabel(\"Feature #\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Kolmogorov-Smirnov\")\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "## Plot zoomed ordered p-values and rejection line\n",
    "Since the intersection of the ordered p-values and the rejection line is not clearly visible, a zoomed plot is provided."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Mann-Whitney-U"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ci/SYXbv2wNu7EgDh4WE88cRTtG//BADe3l34/fffWLNmFaGhoy32W7lyOT17vkTz5q0A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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x114f9f610>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_rejected_index = (df_pvalues_mann[\"relevant\"] == True).sum() - 1\n",
    "margin = 20\n",
    "a = max(last_rejected_index - margin, 0)\n",
    "b = min(last_rejected_index + margin, len(df_pvalues_mann) - 1)\n",
    "\n",
    "df_pvalues_mann[a:b].p_value.where(df_pvalues_mann[a:b].relevant)\\\n",
    "    .plot(style=\".\", label=\"relevant features\")\n",
    "df_pvalues_mann[a:b].p_value.where(~df_pvalues_mann[a:b].relevant)\\\n",
    "    .plot(style=\".\", label=\"irrelevant features\")\n",
    "plt.plot(np.arange(a, b), rejection_line_mann[a:b], label=\"rejection line (FDR = \" + str(FDR_LEVEL) + \")\")\n",
    "plt.xlabel(\"Feature #\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Mann-Whitney-U\")\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### Kolmogorov-Smirnov"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t1FMfS0RERERETIOSaTGIjIygS5d2rF+/BVfXEtSvX5OePd9m69ZNlC37Ii1b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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x117ea9510>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "last_rejected_index = (df_pvalues_smir[\"relevant\"] == True).sum() - 1\n",
    "margin = 20\n",
    "a = max(last_rejected_index - margin, 0)\n",
    "b = min(last_rejected_index + margin, len(df_pvalues_smir) - 1)\n",
    "\n",
    "df_pvalues_smir[a:b].p_value.where(df_pvalues_smir[a:b].relevant)\\\n",
    "    .plot(style=\".\", label=\"relevant features\")\n",
    "df_pvalues_smir[a:b].p_value.where(~df_pvalues_smir[a:b].relevant)\\\n",
    "    .plot(style=\".\", label=\"irrelevant features\")\n",
    "plt.plot(np.arange(a, b), rejection_line_smir[a:b], label=\"rejection line (FDR = \" + str(FDR_LEVEL) + \")\")\n",
    "plt.xlabel(\"Feature #\")\n",
    "plt.ylabel(\"p-value\")\n",
    "plt.title(\"Kolmogorov-Smirnov\")\n",
    "plt.legend()\n",
    "plt.plot()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.11"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}
